Abstract-- Wireless Sensor Networks are highly distributed
self-organized systems. WSN have been deployed in various
fields. Now a day, Topology issues have received more and
more attentions in Wireless Sensor Networks (WSN). While
WSN applications are normally optimized by the given
underlying network topology, another trend is to optimize
WSN by means of topology control. In this area, a number
of approaches have been invested, like network connectivity
based topology control, cooperating schemes, topology
directed routing, sensor coverage based topology control.
Most of the schemes have proven to be able to provide a
better network monitoring and communication performance
with prolonged system lifetime. In this survey paper, I
provide a full view of the studies in this area.

To achieve a lasting and scalable WSN design, the
some aspects have to be carefully taken into account in the
design stage.
II. TOPOLOGY ISSUES, THE TAXONOMY
There are mainly two categories of topology issues.
1)
2)

Topology Control Problems and,
Topology Awareness Problems

I. INTRODUCTION
Wireless Sensor Networks (WSNs) have become an
emerging technology that has a wide range of potential
applications including traffic control, object tracking,
scientific observing and forecasting, and environment
monitoring etc.

Fig. 3: A Taxonomy of topology issues in WSNs
A. Topology Control Problems
It can be further divided into two categories:

.

Fig. 1: WSN Topologies
A WSN normally consists of a large number of
distributed nodes that organize themselves into a multi-hop
wireless network and typically these nodes coordinate to
perform a common task.

Unstructured
and timevarying
network
topology

Lowquality
communic
ations
Data
processing

Energy
conservatio
n

Scalability

Operation
in hostile
environme
nts

Limited
bandwidth

Fig. 2: Aspects need to be taken care of

1) Sensor Coverage Topology
The coverage topology describes the topology of sensor
coverage and is concerned about how to maximize a reliable
sensing area while consuming less power. There are three
categories into Sensor Coverage Topology: Static Network,
Mobile Network and Hybrid Network.
 Static Network:
For a static sensor network, proposed approaches have
different coverage objectives. I introduce these approaches
separately.
 Partial Coverage:
WSN system functioning time by keeping only a necessary
set of sensors working in case the node deployment density
is much higher than necessary. PEAS protocol consists of
two algorithms: Probing Environment and Adaptive
Sleeping.
 Single Coverage:
The Optimal Geographical Density Control (OGDC)
protocol tries to minimize the overlap of sensing areas of all
sensor nodes for cases when Rc ≥ 2Rs where Rc is the node
communication range and Rs is the node sensing range.
OGDC is a fully localized algorithm but the node location is
needed as a pre-knowledge.
 Multiple Coverage:
A distributed density control algorithm based on time
synchronization among the neighbors. A node can decide its

on-duty time such that the whole grid still gets the required
degree of coverage.
 Mobile Network:
The deployment schemes for movable sensors. Given an
area to be monitored, the proposed distributed selfdeployment protocols first discover the existence of
coverage holes in the target area then calculate the target
positions and move sensors to diminish the coverage holes.
The sensor network in the viewpoint of virtual forces, nodes
only use their sensed information to make moving
decisions. It is a cost effective and no communication
among the nodes or localization information is needed. For
the DSS (Distributed Self-Spreading) algorithm proposed,
sensors are randomly deployed initially. They start moving
based on partial forces exerted by the neighbors. The forces
exerted on each node by its neighbors depend on the local
density of deployment and on the distance between the node
and the neighbor.
 Hybrid Network:
The coverage scenario with only some of the sensors are
capable of moving has been under active research,
especially in the field of robotics for exploration purpose.
The movement capable sensors can help in deployment and
network repair by moving to appropriate locations within
the field to achieve desired level of coverage. Combined
solution for the exploration and coverage of a given target
area. The coverage problem is solved with the help of a
constantly moving robot in a given target area. The
algorithm does not consider the communications between
the deployed nodes. All decisions are made by the robot by
directly communicating with a neighbor sensor node. Single
coverage problem by moving the available mobile sensors
in a hybrid network to heal coverage holes
2) Sensor Connectivity Topology
The connectivity topology on the other hand is more
concern more about network connectivity and emphasizes
the message retrieve and delivery in the network. Two kinds
of mechanisms have been utilized to maintain an efficient
sensor connectivity topology:

 Power Control Mechanisms:
The goal of power control mechanisms is to dynamically
change the nodes’ transmitting range in order to maintain
some property of the communication graph, while reducing
the energy consumed by node transceivers because they are

one of the primary sources of energy consumption in WSNs.
Power control mechanisms are fundamental to achieving a
good network energy efficiency. Power control is studied in
homogeneous and non-homogeneous scenarios which can
be distinguished by examine if the nodes have the same
transmitting range or not. For homogeneous network, the
CTR (Critical Transmitting Range) problem has been
investigated in theoretical ways as well as practical
viewpoints. A distributed protocol, called COMPOW that
attempts to determine the minimum common transmitting
range needed to ensure network connectivity. They show
that setting the transmitting range to this value has the
beneficial effects of maximizing network capacity, reducing
the contention to access the wireless channel, and
minimizing energy consumption. the tradeoff between the
transmitting range and the size of the largest connected
component in the communication graph. The experimental
results presented show that, in sparse two and threedimensional networks, the transmitting range can be reduced
significantly if weaker requirements on connectivity are
acceptable: halving the critical transmitting range, the
largest connected component has an average size of
approximately 0.9n. This means that a considerable amount
of energy is spent to connect relatively few nodes. Nonhomogeneous networks are more challenging because nodes
are allowed to have different transmitting ranges. The
problem of assigning a transmitting range to nodes in such a
way that the resulting communication graph is strongly
connected and the energy cost is minimum is called the
Rang Assignment (RA) problem. It is shown to be NP-hard
in the case of 2D and 3D networks. However the optimal
solution can be approximated within a factor of 2 using the
range assignment generated. An important variant of RA has
been recently studied is based on the concept of symmetry
of the communication graph. Due to the high overhead
needed to handle unidirectional links in routing protocols or
MAC protocols which are naturally designed to work under
the symmetric assumption, Symmetric Range Assignment
(SRA) shows more practical significance. However, SRA
remains NP-hard in 2D and 3D networks, and it even incurs
a considerable additional energy cost over RA. I can refine
SRA to WSRA (Weakly Symmetric Range Assignment)
which weakens the requirement that the communication
graph contains only bidirectional links by allowing the
existence of the unidirectional links but requiring the
symmetric sub graph of the communication graph resulting
from RA connected. In the released WSRA problem, only
marginal effect on the energy cost has been induced while
the desired symmetry property has been kept.
 Power Management Mechanisms:
Power management is concerned of which set of nodes
should be turned on/off and when, for the purpose of
constructing energy saving topology to prolong the network
lifetime. It can utilize information available from all the
layers in the protocol stack. In GAF approach, nodes use
location information to divide the field into fixed square
grids. The size of each grid stays constant, regardless of
node density. Nodes within a grid switch between sleeping
and listening mode, with the guarantee that one node in each
grid stays up so that a dynamic routing backbone is
maintained to forward packets. Span, a power saving
topology maintenance algorithm for multi-hop ad hoc
All rights reserved by www.ijsrd.com

wireless networks which adaptively elects coordinators from
all nodes to form a routing backbone and turn off other
nodes’ radio receivers most of the time to conserve power.
STEM approach, which exploits the time dimension rather
than the node density dimension to control a power saving
topology of active nodes. They switch nodes between two
states, “transfer state” and “monitoring state”. Data are only
forwarded in the transfer state. In the monitoring state,
nodes remain their radio off and will switch into transfer
state to be an initiator node on event detected. The extended
study on combining STEM and GAF shows the potential of
further power saving by exploiting both time dimension and
node density dimension.
B. Topology Awareness Problems
It includes geographic routing problems and sensor holes
problems.
Geographic routing uses geographic and
topological information of the network to achieve optimal
routing schemes with high routing efficiency and low power
consumption.
Various sensor holes, such as Jamming holes,
sink/black holes and worm holes, may form in a WSN and
create network topology variations which trouble the upper
layer applications. For examples, intense communication
may cause jamming holes which will fail to deliver message
to exterior nodes. Sink/Black holes and worm holes are
caused by nodes exhausted around sink node or pretended
sinks or by malicious nodes. If sensor holes issues are not
treated carefully, they will create costly routing table and
exhaust the intermediate nodes rapidly.
 Geographic Routing:
Geographic routing approach relies on greedy forwarding to
route packets based on nodes’ local information on the
network topology. The protocol starts in greedy forwarding
mode, and assumes the location information of sensor nodes
can be obtained by supporting systems. GPSR recovers from
local maximum position by using perimeter rouging mode
and the right-hand rule. kis et al. propose the Compass
Routing algorithm and FACE-1 algorithms that guarantees
the destination is reached even when local minimum
phenomenon occurs in greedy forwarding. Similar to the
work in, Bose et al. propose the FACE-2 routing algorithm.
In contrast to GPSR, routing in FACE-2 is done through the
perimeter of the Gabriel Graph (CG) formed at each node. It
also modifies the FACE-1 so that the perimeter traversal
follows the next edge whenever that edge crosses the line
from the source to destination. Obviously, the downside of
FACE-2 is that it consumes more energy in the perimeter
nodes.
 Hole Problems:
For most of the geographic routing schemes, a routing hole
consists of a region in the sensor network, where either node
are not available or the available nodes cannot participate in
the actual routing of the data due to various possible
reasons. In order to prevent the infection to the packet
delivery by sensor holes, the geographic routing schemes do
not provide methods to detect and localize the holes.
Theoretical work on determining sensor holes in which socalled stuck node is defined and an algorithm called
BOUNDHOLE is proposed to find the holes utilizing the
strong stuck nodes. Study an application specific scenario
for the underground monitoring in coal mine. They propose

a topology maintenance protocol SASA, which claims to
rapidly detect the structure variation during underground
collapse by regulating the mesh sensor network deployment
and formulating a collaborating mechanism based on the
regular beacon strategy for sensors. The so-called edge
nodes outline the sensor hole and report it to the sink. To the
best of our knowledge, the SASA protocol is the first work
which relates the topology variation to the actual
geographical changes. Wood et al. discuss jamming hole in.
The resilience of various routing protocols and energy
conserving topology maintenance algorithms against sink
holes. They showed that popular routing protocols like
directed diffusion, rumor routing and multi-path variant of
directed diffusion etc. are all vulnerable to sink holes
attacks. For geographical greedy forwarding algorithms it is
more difficult to create sink holes because in this case a
malicious node has to advertise different attractive locations
to different neighbors in order to qualify as next hop.
III. CONCLUSIONS
Two major issues found in topology in WSNs, namely
topology awareness and topology control. Topology
awareness problems construct applications or upper
protocols to conform the underlying topology. Typical
approaches applied in this category do not actively consider
improving the topology itself for the specific applications.
Topology control mechanisms focus more on constructing
an energy-efficient and reliable network topology and
normally do not touch individual applications.
Power control and power management are two
different types of topology controlling methods. By focusing
on integrating power control and power management, it is
possible to provide noticeable improvements on network
topology and efficiencies of energy usage.
REFERENCES
[1] Pirmez, L., Delicato, F., Pires, P., Mostardinha, A., de
Rezende, N.:2007 Applyingfuzzy logic for decisionmaking on wireless sensor networks. In: Fuzzy Systems
Conference '07, Proc., IEEE, 2007.
[2] Wolenetz, M., Kumar, R., Shin, J., Ramachandran,
U.:2005 A simulation-based study of wireless sensor
network middleware. Network Management 15(4),
2005
[3] B. Karp and H. T. Kung, "Greedy Perimeter Stateless
Routing for Wireless Networks," in proceedings of
theSixth Annual International Conference on Mobile
Computing and Networking (Mobicom), 2000.
[4] F. Ye, G. Zhong, S. Lu and L. Zhang, "PEAS: A Robust
Energy Conserving Protocol for Long-lived Sensor
Networks," in proceedings of International Conference
on Distributed Computing Systems (ICDCS), 2003.
[5] H. Zhang and J. Hou, "Maintaining Sensing Coverage
and Connectivity in Large Sensor Networks,"
Department of Computer Science, UIUC UIUCDCS-R2003-2351, 2003
[6] G. Wang, G. Cao and T. L. Porta, "Movement-Assisted
Sensor Deployment," in proceedings of IEEE
INFOCOM, 2004
[7] X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, et al.,
"Integrated Coverage and Connectivity Configuration in